numpy reshape confusion with negative shape values

Question

Always confused how numpy reshape handle negative shape parameter, here is an example of code and output, could anyone explain what happens for reshape [-1, 1] here? Thanks.

Related document, using Python 2.7.

http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html

import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

S = np.array(['box','apple','car'])
le = LabelEncoder()
S = le.fit_transform(S)
print(S)
ohe = OneHotEncoder()
one_hot = ohe.fit_transform(S.reshape(-1,1)).toarray()
print(one_hot)

[1 0 2]
[[ 0.  1.  0.]
 [ 1.  0.  0.]
 [ 0.  0.  1.]]

Answer 1

-1 is used to infer one missing length from the other. For example reshaping (3,4,5) to (-1,10) is equivalent to reshaping to (6,10) because 6 is the only length that makes sense form the other inputs.

Answer 2

From reshape docs:

One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.

In your case it is used for the common task of transforming the (3,) S into a (3,1) array. I think in this particular case using S[:, None] would have the same effect.